CN109685683A

CN109685683A - For general energy scheduling model optimization method, apparatus, medium and the equipment that can be stood

Info

Publication number: CN109685683A
Application number: CN201811507969.2A
Authority: CN
Inventors: 代景龙
Original assignee: Xinao Shuneng Technology Co Ltd
Current assignee: Xinao Shuneng Technology Co Ltd
Priority date: 2018-12-11
Filing date: 2018-12-11
Publication date: 2019-04-26

Abstract

The invention discloses a kind of for general energy scheduling model optimization method, apparatus, readable medium and the electronic equipment that can be stood, this method comprises: establishing the general corresponding energy scheduling model that can stand；The energy scheduling model is solved using branch-bound algorithm, with the optimal solution of the determination energy scheduling model.Scheme proposed by the present invention, algorithm personalisation process ability is higher, can accurately be solved for the general energy scheduling model that can be stood, and obtain the optimal solution of the general energy scheduling model that can be stood.

Description

Energy scheduling model optimization method, device, medium and equipment for universal energy station

Technical Field

The invention relates to the field of energy, in particular to an energy scheduling model optimization method, device, medium and equipment for a universal energy station.

Background

The modern society faces the double pressure of energy crisis and environmental pollution, the ubiquitous energy station is proposed as an efficient distributed energy system which utilizes coupling mechanisms of different forms of energy such as electricity, gas, cold and heat in space and time to realize multi-energy complementation, a corresponding energy scheduling model is generally configured for the ubiquitous energy station, the optimal solution of the energy scheduling model is obtained, and the optimal energy scheduling model of the ubiquitous energy station is obtained according to the optimal solution of the energy scheduling model.

At present, a Complex Programmable Logic Element (CPLEX) or a complex solver such as a Gurobi is generally used for modeling an energy scheduling model and solving an optimal solution of the energy scheduling model, but the complex solver such as the CPLEX or the complex solver is a commercial package, and the algorithm has low personalized processing capability, so that the energy scheduling model of the universal energy station cannot be accurately solved, and the optimal solution of the energy scheduling model of the universal energy station cannot be obtained.

Disclosure of Invention

The invention provides an energy scheduling model optimization method, an energy scheduling model optimization device, a readable medium and electronic equipment for a universal energy station.

In a first aspect, the invention provides an energy scheduling model optimization method for a universal energy station, which comprises the following steps:

establishing an energy scheduling model corresponding to the universal energy station;

and solving the energy scheduling model by using a branch-and-bound algorithm to determine the optimal solution of the energy scheduling model.

Preferably, the first and second electrodes are formed of a metal,

the establishing of the energy scheduling model corresponding to the universal energy station comprises the following steps:

determining an objective function and at least one constraint condition;

establishing the energy scheduling model of the universal energy station by using the determined objective function and the at least one constraint condition.

Preferably, the first and second electrodes are formed of a metal,

the objective function includes:

wherein,

the product of the electricity price and the purchase electricity quantity of the universal energy station in the characterization time period t;

the product of the natural gas price and the amount of the natural gas purchased by the universal energy station in the characterization time t;

the product of the electric quantity required by the user and the electricity price is represented in a characterization time t;

representing the product of the heat quantity required by the user and the heat price in a time period t;

ny represents the number of waste heat boilers, Nm represents the number of internal combustion engines and the number of Nb-meter gas-steam boilers;

the operation and maintenance cost of the ith waste heat boiler in the characterization time period t,The operation and maintenance cost of the ith internal combustion engine in the characterization time t,Representing the operation and maintenance cost of the ith gas steam boiler in the time period tth;

the starting cost of the ith waste heat boiler in the characterization time period t,The starting cost of the ith internal combustion engine in the characterization time period t,Representing the starting cost of the ith gas steam boiler in the time period tth;

the shutdown cost of the ith waste heat boiler in the characterization time t,The shutdown cost of the ith internal combustion engine in the characterization time t,Representing the shutdown cost of the ith gas steam boiler in the time period tth;

state variables of the ith exhaust-heat boiler in the characterization time t,State variables characterizing the ith internal combustion engine in the time period t,Representing state variables of the ith gas steam boiler in the time period tth;

the starting variable of the ith waste heat boiler in the characterization time period t,A starting variable of the ith internal combustion engine in the characterization time t,Representing a starting variable of an ith gas steam boiler in a time period t;

the shutdown variable of the ith exhaust-heat boiler in the characterization time t,A shutdown variable characterizing the ith internal combustion engine,Characterization time t ith table gas steamShutdown variables for the boiler;

the state variable, the startup variable and the shutdown variable are all integer variables of 0 or 1.

Preferably, the first and second electrodes are formed of a metal,

the at least one constraint includes: at least one of electric quantity balance constraint condition, heat energy balance constraint condition, gas balance constraint condition, internal combustion engine waste heat recovery constraint condition, equipment operation safety and state coupling constraint condition, startup and shutdown and equipment state coupling constraint, and input and output coupling constraint condition between equipment;

the power balance constraint conditions comprise:

wherein,the characterization time t is the purchased electric quantity of the energy station,The electricity generation quantity of the ith internal combustion engine in the characterization time t,Representing the electric quantity required by a user at the moment t, and representing the number of the internal combustion engines by Nm;

the thermal energy balance constraints include:

wherein,the high-temperature steam quantity produced by the ith gas steam boiler in the characterization time t,The high-temperature steam quantity generated by the ith waste heat boiler in the characterization time t,Representing the heat required by a user in a time period t, representing the number of the gas steam boilers by Nb, and representing the number of the waste heat boilers by Ny;

the gas balance constraint conditions comprise:

wherein,the characterization time t is the natural gas amount used by the ith gas steam boiler,The amount of natural gas used by the ith internal combustion engine at the characterization time t,The amount of natural gas purchased by the station at the characterization time t;

the internal combustion engine waste heat recovery constraint conditions comprise:

wherein s represents high-temperature flue gas,The high-temperature smoke gas produced by the ith internal combustion engine at the characterization time t,The high-temperature flue gas input amount of the ith waste heat boiler in the characterization time t,State variables characterizing the ith internal combustion engine in the time period t,Representing state variables of the ith waste heat boiler in the time period t;

the device operation safety and state coupling constraint conditions comprise:

wherein,state variable of the ith gas steam boiler in the characterization time period t,Representing the high-temperature steam production amount of the ith internal combustion engine in the time t;

the boot-up and shutdown and equipment state coupling constraint conditions comprise:

wherein,the starting variable of the ith gas steam boiler is represented in the characterization time period t,A starting variable of the ith internal combustion engine in the characterization time t,The starting variable of the ith waste heat boiler in the characterization time period t,The shutdown variable of the ith gas steam boiler is represented in the period t,A shutdown variable characterizing the ith internal combustion engine,The shutdown variable of the ith exhaust-heat boiler in the characterization time t,State variable of the ith gas steam boiler for representing the time period t +1,State variables characterizing the ith internal combustion engine in a time period t +1,Representing the state variable of the ith exhaust-heat boiler in the time period t + 1;

the input-output coupling constraint conditions between the devices comprise: the input and output of the internal combustion engine and/or the input and output of the gas steam boiler are coupled; wherein,

internal combustion engine input-output coupling:

and/or the presence of a gas in the gas,

the input and output of the gas steam boiler are coupled:

wherein,the characterization time tth gas-steam boiler uses the natural gas quantity,High-temperature steam production amount d of jth gas steam boiler in characterization time period t_i、e_i、a_j、b_jAll characterize the fit value of the historical data,

Preferably, the first and second electrodes are formed of a metal,

solving the energy scheduling model by using a branch-and-bound algorithm to determine an optimal solution of the energy scheduling model, including:

s1, initializing a first objective function value corresponding to a feasible solution of the objective function under the at least one constraint condition in the energy scheduling model, determining the first objective function value as a current upper bound of the objective function, and performing relaxation processing on the energy scheduling model to form a linear relaxation scheduling model;

s2, solving a first optimal solution of the linear relaxation scheduling model;

s3, judging whether each integer variable carried in the first optimal solution of the linear relaxation scheduling model is an integer, if so, executing S11, otherwise, executing S4;

s4, selecting a first integer variable with an optimal value being a non-integer from the first optimal solution, and performing integer branching on the selected first integer variable to form two branch submodels;

s5, selecting an unselected branch submodel, solving a second optimal solution of the selected branch submodel, and calculating a current function value of the second optimal solution of the branch submodel corresponding to the objective function;

s6, detecting whether the current function value is larger than the current upper bound, if so, discarding the selected branch submodel and executing S9, otherwise, executing S7;

s7, checking whether the optimal values of the integer variables carried in the second optimal solution are integers, if yes, determining that the second optimal solution is a new current upper bound, and executing S9, otherwise, executing S8;

s8, selecting a second integer variable with an optimal value being non-integer from the second optimal solution, carrying out integer branching on the selected second integer variable to form two new branch submodels, and executing S5;

s9, detecting whether branch submodels which are not selected exist, if yes, executing S5, otherwise, executing S10;

s10, determining a second optimal solution corresponding to the current upper bound as an optimal solution of the energy scheduling model;

s11, determining the first optimal solution of the linear relaxation scheduling model as the optimal solution of the energy scheduling model.

In a second aspect, the invention provides an energy scheduling model optimization device for a universal energy station, which comprises:

the modeling module and the solving processing module;

the modeling module is used for establishing an energy scheduling model corresponding to the universal energy station;

and the solving processing module is used for solving the energy scheduling model by using a branch-and-bound algorithm so as to determine the optimal solution of the energy scheduling model.

Preferably, the first and second electrodes are formed of a metal,

the modeling module includes: the system comprises an objective function construction unit, a constraint condition construction unit and a modeling unit;

the target function constructing unit is used for constructing a target function;

the constraint condition construction unit is used for constructing at least one constraint condition;

the modeling unit is used for establishing the energy scheduling model according to the objective function and the at least one constraint condition.

Preferably, the first and second electrodes are formed of a metal,

the objective function includes:

wherein,

the shutdown variable of the ith exhaust-heat boiler in the characterization time t,A shutdown variable characterizing the ith internal combustion engine,Characterizing shutdown variables of the ith gas steam boiler in a time period t;

Preferably, the first and second electrodes are formed of a metal,

the power balance constraint conditions comprise:

the thermal energy balance constraints include:

the gas balance constraint conditions comprise:

the device operation safety and state coupling constraint conditions comprise:

internal combustion engine input-output coupling:

and/or the presence of a gas in the gas,

the input and output of the gas steam boiler are coupled:

Preferably, the first and second electrodes are formed of a metal,

the solving processing module is used for executing the following steps S1-S11:

s2, solving a first optimal solution of the linear relaxation scheduling model;

In a third aspect, the invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the method according to any of the first aspect.

In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.

The invention provides an energy scheduling model optimization method, an energy scheduling model optimization device, a readable medium and electronic equipment for a universal energy station, the method solves the energy scheduling model by utilizing the branch-and-bound algorithm after constructing the energy scheduling model of the universal energy station, the branch-and-bound algorithm can repeatedly divide all feasible regions of the energy scheduling model, so that the subsets obtained by segmentation become smaller and smaller, and a target lower bound is calculated for the solution set in each subset and compared with the initially set upper bound, and the subset of the target lower bound which is larger than the initially set upper bound is not further divided, so that the search range is reduced, the algorithm has higher personalized processing capability, so the branch-and-bound algorithm is utilized to solve the energy scheduling model of the universal energy station, the energy scheduling model of the universal energy station can be accurately solved, and the optimal solution of the energy scheduling model of the universal energy station can be obtained.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

FIG. 1 is a schematic flow chart of an energy scheduling model optimization method for a universal energy station according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of an energy scheduling model optimizing apparatus for a universal energy station provided in an embodiment of the present invention;

fig. 3 is a schematic structural diagram of another energy scheduling model optimizing apparatus for a universal energy station provided in an embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 1, an embodiment of the present invention provides an energy scheduling model optimization method for a universal energy station, including:

step 101, establishing an energy scheduling model corresponding to the universal energy station;

and 102, solving the energy scheduling model by using a branch-and-bound algorithm to determine an optimal solution of the energy scheduling model.

The embodiment of the invention provides an energy scheduling model optimization method for a universal energy station, which comprises the steps of constructing an energy scheduling model of the universal energy station, solving the energy scheduling model by using a branch and bound algorithm, wherein the branch and bound algorithm can repeatedly divide all feasible regions of the energy scheduling model, so that the subsets obtained by segmentation become smaller and smaller, and a target lower bound is calculated for the solution set in each subset and compared with the initially set upper bound, and the subset of the target lower bound which is larger than the initially set upper bound is not further divided, so that the search range is reduced, the algorithm has higher personalized processing capability, so the branch-and-bound algorithm is utilized to solve the energy scheduling model of the universal energy station, the energy scheduling model of the universal energy station can be accurately solved, and the optimal solution of the energy scheduling model of the universal energy station can be obtained.

In an embodiment of the present invention, the establishing an energy scheduling model corresponding to the universal energy station includes: determining an objective function and at least one constraint condition; establishing the energy scheduling model of the universal energy station by using the determined objective function and the at least one constraint condition.

In the above embodiment, the user may construct the objective function and the constraint condition according to actual requirements.

In one embodiment of the present invention, the objective function includes:

wherein,

In the above embodiment, the factors considered when constructing the objective function F are three energy sources (i.e. electric energy, natural gas and heat energy) and three devices (i.e. internal combustion engine, waste heat boiler and gas steam boiler) in the universal power station, and the interrelation between the three energy sources and the three devices in the universal power station is as follows: the internal combustion engine uses natural gas as a raw material to generate electric quantity and high-temperature flue gas, the electric quantity generated by the internal combustion engine is used for providing electric energy for a user, the generated high-temperature flue gas is changed into high-temperature steam through a waste heat boiler to provide heat energy for the user, a gas steam boiler uses natural gas as a raw material to generate high-temperature steam to provide heat energy for the user, an energy-flooding station purchases natural gas to provide raw materials for the internal combustion engine and the gas steam boiler, and meanwhile the energy-flooding station also purchases electric quantity to maintain normal operation of the energy-flooding station.

In one embodiment of the present invention, the at least one constraint condition includes: at least one of electric quantity balance constraint condition, heat energy balance constraint condition, gas balance constraint condition, internal combustion engine waste heat recovery constraint condition, equipment operation safety and state coupling constraint condition, startup and shutdown and equipment state coupling constraint, and input and output coupling constraint condition between equipment;

the power balance constraint conditions comprise:

the thermal energy balance constraints include:

the gas balance constraint conditions comprise:

the device operation safety and state coupling constraint conditions comprise:

internal combustion engine input-output coupling:

and/or the presence of a gas in the gas,

the input and output of the gas steam boiler are coupled:

In the above embodiment, with respect to formula 2, since the electric quantity generated by the internal combustion engine in the universal energy station and the electric quantity purchased from the universal energy station to the power grid should satisfy the electric quantity provided by the universal energy station to the user, that is, the objective function should satisfy the electric quantity balance, formula 2 may be used as a constraint condition in the energy scheduling model; with respect to formula 3, because the internal combustion engine in the universal energy station generates electric quantity and simultaneously generates high-temperature flue gas, the high-temperature flue gas is changed into high-temperature steam through the waste heat boiler, the high-temperature steam generated by the waste heat boiler and the high-temperature steam generated by the gas steam boiler meet the heat requirement of a user, namely, an objective function should meet heat balance, formula 3 can be used as a constraint condition in an energy scheduling model; for formula 4, because the internal combustion engine and the gas steam boiler both use natural gas as raw materials, the universal energy station provides raw materials for the internal combustion engine and the gas steam boiler by purchasing natural gas, that is, the objective function should satisfy gas balance, therefore, formula 4 can be used as a constraint condition in the energy scheduling model; with respect to formula 5, because all the high-temperature flue gas generated by the internal combustion engine is output to the waste heat boiler, that is, the output high-temperature flue gas amount of the internal combustion engine is consistent with the input high-temperature flue gas amount of the waste heat boiler, and the operating states of the internal combustion engine and the waste heat boiler are always kept the same, the objective function should satisfy the balance of the high-temperature flue gas and satisfy the same state variables of the internal combustion engine and the waste heat boiler, so that formula 5 can be used as a constraint condition in the energy scheduling model; aiming at the formula 6, the maximum capacity and the minimum capacity of each device are considered, and the maximum capacity and the minimum capacity of the gas steam boiler, the internal combustion engine and the waste heat steam boiler are respectively coupled with the state variables of the devices, namely, the objective function should meet the relationship between the operation safety and the state coupling of the devices, so the formula 6 can be used as a constraint condition in the energy scheduling model; aiming at the formula 7, a starting variable and a stopping variable are introduced, and the coupling between the startup and the shutdown of the equipment and the equipment state in each time interval is considered, namely, the target function should meet the coupling relation between the startup and the shutdown of the equipment and the equipment state in each time interval, so that the formula 7 can be used as a constraint condition in an energy scheduling model; aiming at the formula 8, the internal combustion engine consumes the natural gas to generate electric quantity, the relation between the consumption of the natural gas of the internal combustion engine and the generated electric quantity is considered, and the input-output coupling relation of the internal combustion engine is obtained by fitting through historical data of the internal combustion engine, namely the target function should meet the input-output coupling relation of the internal combustion engine, so the formula 8 can be used as a constraint condition in an energy scheduling model; for formula 9, because the gas-steam boiler consumes natural gas and generates high-temperature steam, the historical data is used for fitting to obtain an input-output coupling relation of the gas-steam boiler, that is, an objective function should satisfy the input-output coupling relation of the gas-steam boiler, and therefore, formula 9 can be used as a constraint condition in the energy scheduling model.

Obviously, an energy scheduling model of the universal energy station is formed by formulas 1 to 9, a state variable, a starting variable and a stopping variable are all integer variables of 0 or 1, 1 represents an equipment running state, a starting action or a stopping action, and 0 represents an equipment stopping state, a starting action or an unrelated action, so that the energy scheduling model of the universal energy station is optimized and belongs to mixed integer linear programming; and optimizing the energy scheduling model of the universal energy station, namely solving the optimal solution of the objective function under the constraint condition, and obtaining the optimal scheduling model of the universal energy station according to the optimal solution of the energy scheduling model so as to realize the optimization goal of maximizing the benefit.

In an embodiment of the present invention, the solving the energy scheduling model by using a branch-and-bound algorithm to determine an optimal solution of the energy scheduling model includes:

s2, solving a first optimal solution of the linear relaxation scheduling model;

In the above embodiment, the branch-and-bound algorithm may repeatedly divide all feasible regions of the energy scheduling model under the constraint condition, so that the subsets obtained by the division become smaller and smaller, calculate a target lower bound for the solution set in each subset, compare the target lower bound with the initially set upper bound, and do not further divide the subset whose target lower bound is greater than the initially set upper bound, thereby reducing the search range and finally determining the optimal solution of the energy scheduling model.

Specifically, a first objective function value corresponding to a feasible solution of an objective function under a constraint condition in an energy scheduling model is initialized, and the first objective function value is determined as a current upper bound of the objective function; then, relaxation processing is carried out on the energy scheduling model of the universal energy station, namely, the state variable, the starting variable and the stopping variable in the objective function can only be integer variables of 0 or 1, and the relaxation is continuous variables from 0 to 1, so that a linear relaxation scheduling model is formed; solving a first optimal solution of the linear relaxation scheduling model, wherein if the optimal values of the integer variables carried in the first optimal solution of the linear relaxation scheduling model are integers, the first optimal solution is the optimal solution of the energy scheduling model, if the first optimal solution of the linear relaxation scheduling model has integer variables with optimal values and non-integers, selecting one first integer variable with optimal values and non-integers from the first optimal solution, integer branching is performed on the first integer variable to form two branch submodels, for example, if the optimal value of the state variable of the ith internal combustion engine in the first optimal solution is 0.4, the state variable of the ith internal combustion engine may be selected as the first integer variable, integer branching is carried out on the data to form a branch submodel of which the state variable of the ith internal combustion engine is 0 and a branch submodel of which the state variable of the ith internal combustion engine is 1; selecting all branch submodels to solve to obtain a second optimal solution, if the current function value of the second optimal solution of the obtained branch submodels on the objective function is larger than the current upper bound, abandoning the branch submodel, and only obtaining worse results because of continuously branching; if the obtained optimal solution of the branch sub-model is smaller than the current upper bound, firstly detecting whether the optimal values of the integer variables carried by the second optimal solution are integers, if so, the second optimal solution is a feasible solution of the energy scheduling model, and the current function value of the second optimal solution on the target function is smaller than the current upper bound, and updating the current upper bound; if the second optimal solution has integer variables with optimal values being non-integers, selecting the second integer variables with optimal values being non-integers from the second optimal solution to carry out integer branching to form a new branch sub-model, and continuously solving the second optimal solution of the new branch sub-model until the possibility of continuous branching does not exist; wherein, the second optimal solution is obtained after the first integer variable is subjected to integer branching, namely the first integer variable can only be 0 or 1, and the second integer variable is different from the first integer variable;

and updating the current upper bound when the obtained second optimal solution is a feasible solution of the energy scheduling model and the current function value of the current second optimal solution on the objective function is smaller than the current upper bound, and determining the finally obtained second optimal solution corresponding to the current upper bound as the optimal solution of the energy scheduling model after all the formed branch subproblems are solved.

Based on the same concept as the method embodiment of the present invention, as shown in fig. 2, an embodiment of the present invention further provides an energy scheduling model optimization apparatus for a universal energy station, the apparatus including: a modeling module 201 and a solution processing module 202; the modeling module 201 is used for establishing an energy scheduling model corresponding to the universal energy station; the solving processing module 202 is configured to solve the energy scheduling model by using a branch-and-bound algorithm to determine an optimal solution of the energy scheduling model.

In one embodiment of the present invention, the modeling module 201 includes: an objective function construction unit 2011, a constraint condition construction unit 2012 and a modeling unit 2013; the objective function constructing unit 2011 is configured to construct an objective function; the constraint condition construction unit 2012 is configured to construct at least one constraint condition; the modeling unit 2013 is configured to establish the energy scheduling model according to the objective function and the at least one constraint condition.

In one embodiment of the present invention, the objective function includes:

wherein,

characterization time t ith stationThe state variable of the waste heat boiler,State variables characterizing the ith internal combustion engine in the time period t,Representing state variables of the ith gas steam boiler in the time period tth;

the power balance constraint conditions comprise:

the thermal energy balance constraints include:

the gas balance constraint conditions comprise:

the device operation safety and state coupling constraint conditions comprise:

internal combustion engine input-output coupling:

and/or the presence of a gas in the gas,

the input and output of the gas steam boiler are coupled:

In an embodiment of the present invention, the solution processing module 202 is configured to execute the following steps S1 to S11:

s2, solving a first optimal solution of the linear relaxation scheduling model;

For convenience of description, the above device embodiments are described with functions divided into various units or modules, and the functions of the units or modules may be implemented in one or more software and/or hardware when implementing the present invention.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.

And the memory is used for storing the execution instruction. In particular, a computer program that can be executed by executing instructions. The memory may include both memory and non-volatile storage and provides execution instructions and data to the processor.

In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then runs the corresponding execution instruction, and the corresponding execution instruction can also be obtained from other equipment, so as to form the energy scheduling model optimization device for the universal energy station on a logic level. The processor executes the execution instructions stored in the memory, so that the energy scheduling model optimization method for the universal energy station provided by any embodiment of the invention is realized through the executed execution instructions.

The method executed by the energy scheduling model optimizing device for the universal station according to the embodiments shown in fig. 2 and 3 of the present invention can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

An embodiment of the present invention further provides a readable storage medium, which stores execution instructions, and when the stored execution instructions are executed by a processor of an electronic device, the electronic device can be caused to execute the energy scheduling model optimization method for a universal energy station provided in any embodiment of the present invention, and is specifically configured to execute the method shown in fig. 1.

The electronic device described in the foregoing embodiments may be a computer.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.

The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. An energy scheduling model optimization method for a universal energy station is characterized by comprising the following steps:

2. The method of claim 1,

determining an objective function and at least one constraint condition;

3. The method of claim 2,

the objective function includes:

wherein,

4. The method of claim 2,

the power balance constraint conditions comprise:

the thermal energy balance constraints include:

the gas balance constraint conditions comprise:

the device operation safety and state coupling constraint conditions comprise:

internal combustion engine input-output coupling:

and/or the presence of a gas in the gas,

the input and output of the gas steam boiler are coupled:

5. The method of claim 3,

s2, solving a first optimal solution of the linear relaxation scheduling model;

6. An energy scheduling model optimization device for a universal energy station is characterized by comprising:

the modeling module and the solving processing module;

7. The apparatus of claim 6,

8. The apparatus of claim 7,

the objective function includes:

wherein,

9. The apparatus of claim 7,

the power balance constraint conditions comprise:

the thermal energy balance constraints include:

the gas balance constraint conditions comprise:

the device operation safety and state coupling constraint conditions comprise:

wherein,the starting variable of the ith gas steam boiler is represented in the characterization time period t,A starting variable of the ith internal combustion engine in the characterization time t,The starting variable of the ith waste heat boiler in the characterization time period t,The shutdown variable of the ith gas steam boiler is represented in the period t,A shutdown variable characterizing the ith internal combustion engine,The shutdown variable of the ith exhaust-heat boiler in the characterization time t,State variable of the ith gas steam boiler for representing the time period t +1,State variables characterizing the ith internal combustion engine in a time period t +1,Characterization time t +1 ith waste heat boiler stateA variable;

internal combustion engine input-output coupling:

and/or the presence of a gas in the gas,

the input and output of the gas steam boiler are coupled:

10. The apparatus of claim 8,

the solving processing module is used for executing the following steps S1-S11:

s2, solving a first optimal solution of the linear relaxation scheduling model;

11. A readable medium comprising executable instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the method of any of claims 1 to 5.

12. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-5 when the processor executes the execution instructions stored by the memory.